I'm currently studying how to quantify (differential) privacy in a tight and lossless fashion to maximize the trade-off between privacy and utility. I find this quite interesting, since tighter bounds, especially under composition, can be applied to improve a variety of existing privacy-preserving mechanisms, e.g., modern machine learning methods that often operate on sensitive data in a heavily repeated fashion.

Publications

Mind the overlap between the categories. For a full up-to-date list I recommend checking my Google Scholar page.